Task-Driven Comparison of Topic Models.

Area of Science:

• Natural Language Processing
• Computational Linguistics
• Data Science

Background:

• Topic modeling is a key text analysis technique for extracting thematic content.
• Current topic model comparisons rely on limited numerical metrics, often insufficient for task-specific evaluation.
• Existing methods lack detailed insights into the causes of differences between topic models.

Purpose of the Study:

• To develop a task-centric approach for comparing topic models.
• To provide detailed insights into differences between topic models beyond numerical metrics.
• To support a wider range of analytical tasks in topic model exploration.

Main Methods:

• Deriving comparison tasks from common single-model uses: understanding topics, similarity, and change.
• Developing novel visualization techniques to facilitate these comparison tasks.
• Introducing buddy plots for enhanced visualization of document similarity changes.

Main Results:

• Task-centric comparison offers a more nuanced understanding of topic model performance.
• Visualization techniques effectively support the analysis of topic model differences.
• Buddy plots enable intuitive viewing of document similarity shifts.

Conclusions:

• Task-centric comparison is crucial for effective topic model evaluation.
• Visualizations significantly improve the interpretability of topic model comparisons.
• This approach enhances the practical application of topic modeling in text analysis.